1000 Designs a Day: Neural Concept's Thomas von Tschammer on AI-Native Engineering

0:00Hello, and welcome back to The Cognitive Revolution. Today, my guest is Thomas von Tschammer, co-founder and U.S. managing director of Neural Concept, a Swiss company that uses specialist models for domains like aerodynamics, heat dissipation, and collision safety to help automotive manufacturers and other clients accelerate their product design and engineering processes. As a Detroit, Michigan native, this topic is of particular interest, because my father actually started his career at General Motors in the drafting department, back when designs and assembly instructions were hand-drawn on paper.

0:38And I have vivid memories of watching him use early computer-aided design platforms on Take Your Kid to Work Day back when I was a young boy. The work at the time was still highly manual and often quite intuitive. But as in so many fields, it's become far more computerized over time. By the time my dad retired, designs were routinely tested via physics-based digital simulations before the physical manufacturing process began. And this increased iteration velocity by an order of magnitude. But still, as we've seen in biological structure and binding prediction, material science, and robotics controls, the compute required to run these simulations often becomes a bottleneck unto itself.

1:22Today, as you'll hear, neural concepts models can deliver similar results to expensive physics-based solvers in minutes, and they now also offer an engineering co-pilot product, which can both call these domain-specific prediction models as tools and actually use the core CAD platforms to make design changes as required. This tick-tock combination of agentic optimization and domain-specific validation is the perfect recipe for reinforcement learning. And already today, it allows manufacturers like Jaguar Land Rover to conduct aerodynamic testing on more than 1 ,000 designs per day. It also frees human engineers to explore much larger regions of design space and to focus their attention on navigating higher-level trade-offs that involve other parts of the organization.

2:11Plus, it occasionally produces surprising, Move 37-like designs that actually alert human engineers to new possibilities. NeuroConcept has even found a niche in Formula One racing, which, I was surprised to learn, actually limits the amount of compute that teams can use for aerodynamic optimization from one race week to the next. The bottom line is that we can add engineering to a long list of domains where essentially the same pattern of AI development is working over and over again. What once could only be done manually in the physical world was first digitized, and then dramatically accelerated with specialist models.

2:51Today, agentic workflows are accelerating things further, and neural concept is beginning to evolve from training models on a per-customer basis to a future of more general-purpose foundation models for engineering. All of which makes it pretty easy for me to imagine a future engineering superintelligence that combines the general purpose design skills with these superhuman intuitions, all in the same set of weights. As we reach that point, and probably even before, we can expect faster and faster product cycles and an explosion of new form factors, all with higher quality and better resource efficiency than we've ever experienced before.

3:34If you've ever felt that promises of AI abundance were a bit too hand-wavy or detached from physical reality, I think this episode should serve to inspire you. And so, I hope you enjoy this preview of the AI-powered future of engineering with Thomas von Schalmer of Neural Concept. The Cognitive Revolution is brought to you by Mercury, the fintech that more than 300 ,000 ambitious companies and individuals trust to run their finances. I've wired AI into nearly every corner of my life. My email, my messages, my calendar. I even gave Mercury virtual cards to my agents, with low limits and category and merchant restrictions, for their autonomous use.

4:16But still, my AI's access to my financial data has remained limited. With a normal bank, I might export a bunch of statements and have my assistant process them for me. But for real-time, up-to-date information, and certainly for taking any action, trying to get your agent to use the bank via the browser is just too hard, too slow, and too error-prone to be worth it. And that's why Mercury's new conversational interface, Command, is such a big deal. It's built directly into Mercury, which means you get natural language access to your finances without exposing anything outside of your bank account.

4:51No exports, no spreadsheets, no pasting your transactions into third-party tools. I really think a lot of people are going to prefer it this way. And it can already help you take actions too, with everything bound by the permissions and approval policies that you've already set up in your account. I am genuinely impressed to see this level of AI integration in banking in 2026. And so I invite you to join me in the future. Visit mercury.com to learn more and apply online in minutes. Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., members FDIC.

5:34Thank you to Mercury for supporting the Cognitive Revolution. And now, on with the show. Thomas von Schaumer, co-founder and managing director of the United States at Neural Concept. Welcome to the Cognitive Revolution.

5:48Thomas von Tschammer: Thanks very much. Thank you for listening. I'm excited for this. We have not done much on AI for engineering on this feed. And with 350 episodes under our belts, probably a bit of a miss, especially because I'm sitting here in Detroit, Michigan, where I know you have some customers and where there is a long tradition of engineering physical products for the physical world. My dad, actually, fun fact, worked at GM. He started back when the drafting was still done on pencil paper, big tables with slide rules and stuff like that. And then he moved into the CAD era and now he's retired. And so he's not going to be working through the AI assisted, increasingly AI automated era.

6:37But I still am very excited to kind of pick up where I left off the thread with him on Take Your Kid to Work Day years ago. And fast forward to where we are now. With that in mind, folks who listen to this feed are like very into AI. And there are certain concepts that you don't need to introduce. But I think we probably can't assume common knowledge in terms of what does the life of an engineer, what is the life of a neural concept user look like if you go watch over their shoulder and see them at work for a little representative sampling of their working life. Maybe you could give us a bit of a sense of, in brief, obviously, how do things get designed?

7:18Who's doing that? What are the key skills? What are the key iteration loops look like before AI? And then we'll obviously add that AI layer to our understanding.

7:27Thomas von Tschammer: I can start with the example of the automotive industry, right? Julian is a good example, Nathan. So we've been designing cars essentially for the same way for the past four years, right? Four years ago, before CAN, which is computer-assisted design, when you wanted to design a new car, you were essentially building a prototype, right? And you were crushing that prototype against the wall making sure that you were looking if the pedestrian or the passengers were safe inside the car. And then if not, you'd have to change the design and rebuild a new prototype. As you can imagine, this was a very lengthy process, which means that ultimately you could explore five and ten prototypes a year if you were looking before to go to production.

8:14Thomas von Tschammer: Then about 30, 40 years ago, let's say 40 years ago now, we moved into computer assisted design. Right. Which means that now, four years ago, we could build new designs on the computer directly. And then instead of crushing it in real life against the wall, we could simulate through what's called numerical simulations. The effect of that car crushing against the wall directly on the computer. Right. we use for that software that we call FEA software, finite elements analysis software, that would predict, simulate the effect of the crash. Since now you don't have to build as many prototypes, you could go to maybe 50, 100 of different designs of cars a year, which is substantial, right?

9:00Thomas von Tschammer: Substantial speed. However, these tools, they remain very complex to use and very expensive, right? A single crash simulation can take days to be run because we are solving the questions of physics on the computer. You need very large clusters typically and then you need to wait as an engineer several days, maybe one or two days to get that result out, which today is still the main bottleneck when you want to iterate on your design, right? And there's been improvements, of course, in the algorithms, compute that we have so that we could spin that. But essentially, for the past 30 years, we've been using the same CAD tool and we've been using the same SEA, numerical simulation solvers.

9:45Thomas von Tschammer: And that's true for CRASH, but as I say, for aerodynamics, for thermal management, for every single physics that you need when you build a car. And now, using AI, we're seeing that therm revolution, so from Prodike to CAD and now numerical solvers to AI, where thanks to AI, you don't get results in days, but you get them in minutes. And if you didn't get results in minutes, it means that you don't explore 50 designs a year, maybe 100 designs a year, but now thousands of different options. And that drastically accelerates your development cycles, but also means that you as an engineer can innovate much further because you have more options that you can spring thanks to the datingals so that is a real echo of a pattern that i see across all kinds of different spaces right now where there's this ability for models to learn a sort of intuitive physics i sometimes call it maybe most famously in protein folding right we've had a similarly hard time in the past either doing crystallography to eventually get to a protein structure or doing really compute intensive simulation to get there.

11:04And now somehow with enough data and the magic of learning, we can take a couple orders of magnitude out of the compute that's required. And that just changes the game in terms of how many designs we can explore. So that pattern I think is fairly familiar. What I realized I don't have a great intuition for is like, what are the different flavors of intuitive physics that models that we need to get models to learn in order to accelerate what different subdomains of engineering alluded to one a little bit with aerodynamics. And so I know that'll be prominent on the list, but how many different things are there like this?

11:49And what are the sort of fields to which they apply? What are the unlocks associated with them? And what has Neural Concepts' role been in building these models? Yeah, great question.

12:02Thomas von Tschammer: So there are many different domains, as you can imagine. I mean, think about the complexity of a car, right? Now, today in the industry, a GM or another OEM is accumulating the entire car when developing it, which means that every single component or subassembly within the car is being simulated, is being evaluated and is being iterated on. So that means that we are, as engineers, we are evaluating many different physics on the car. Aerodynamics is one, specifically critical for EVs, right, for the range, you want to improve the range on your next EV. Crash safety for pedestrians and passengers is another big one.

12:43Thomas von Tschammer: Then you also have some management, when we want to cool the batteries, cool the engine, but also for ventilation systems inside the car. Electromagnetism, when we want to build an acceleration of electric motors, there is a big electromagnetism aspect to it. And then structural dynamics, generally speaking for the car, durability of the chassis, or different load profiles, and so on and so forth. And just this thing, the main categories, of course, you can imagine then this is being divided into component sub-assemblies. Ultimately, with a company like GM, you have thousands and thousands of engineers that domain experts on these physics, on these components, so that they can iterate and improve each of these specific assemblies.

13:29Thomas von Tschammer: So those are the big domains, essentially. Are all those different domains that you laid out now powered by a domain specialist model that has learned, for example, the intuitive physics of heat dissipation through a ventilation system? and how, if we just take that one example, if the old version was like actually having a simulation down to the level of, I don't know if it was all this detailed, but going all the way down to molecules of air blowing through a space and how they bounce off of each other and what ultimately happens. Level of abstraction or sort of intuitive physics are we now able to get?

14:12How do we say to this like specialized model, here's a new design for a ventilation system. You predict what's going to happen. What kind of inputs and outputs look like to those models? Are they trained on simulation data also? That's another thing that I've noticed is a real pattern.

14:29Thomas von Tschammer: Yeah. So to the last question, these models, these animals can be trained both on simulation data, but also on test data, external data, right? Because today, even today, there are some phenomenon that we're not able to simulate very accurately with traditional soldiers. In that case, what we can do is that if we cannot simulate them, we can measure them in wing tunnel or in test labs, right? And we can gather the data and then train the corresponding AMOs. So this is also a part of it. This is what we call hybrid training, essentially, where you combine numerical simulation that can be lower fidelity because we are not capturing the physics very well and measurements.

15:07Thomas von Tschammer: Now to your question before, how far have we gone into that space? To be very clear, today we are not fully replacing numerical simulations. The same way that we never fully replaced prototypes, we are still doing prototypes today in the industry, but we are doing much less prototypes and much later in the process. It is going to be the same and it's the same for numerical simulations. We are going to fully replace numerical simulations, but we are going to make a much smarter usage of it for the most mature stages of development. And it's going to be exactly the same with AI. AI, on your own in the development process, we enable you as an engineer to explore a much richer space, to explore the right candidates, and then narrow down the one that you actually want to simulate to validate these different prototypes.

15:59Today,

16:01Thomas von Tschammer: the engineering is such a specific, accurate field that there is not one foundational model that can be used to solve the aerodynamics on every car. Right. There is research definitely going in that direction and we are sort of forefront of it at our concepts. However, it is not yet able to capture the level of EAT that you would need, that every car OEM would need to be able to deploy the shelf. What does that mean? That means that we are retraining and we are training the models, the company specific data and numerical simulations. so a the way that you interact customers if i understand that correctly is models are typically it sounds like maybe trained from scratch on a per customer basis because they are sitting on top of a bunch of the simulation data and some real world test data and they're like man it would be great to take a couple orders of magnitude out of this as we explore the sort of optimization space around the core decisions that we've already made.

17:10So we can't jump from like a sedan to a Cybertruck, perhaps with models that we have available. But once we're in the zone, we kind of know where we're going to end up. We can refine dramatically faster because we're able to train these models on all this existing data and then feel like this is sort of the epicycle development. It's like we're really dialing things in at the end.

17:37Thomas von Tschammer: Okay, exactly. So think about data as knowledge, right? Know-how. So essentially you can, and we also provide for some specific applications, pre-trained models, right? That we're trained from existing data elsewhere. But today we need to fine-tune these models with the company's specific data because they have their own know-how, their best practices, and you want the model to match exactly their requirements. Now, what is very interesting as well is that these models are always evolving over time in the sense that every time you fill them your data, they are being retrained and improved so that they can cover a broader and broader space and become more and more accurate.

18:20Thomas von Tschammer: It's also a way for the company to retain knowledge and know-how, because now every single data point that they generate, this is knowledge that is being captured by the model, and reused such that the next development cycle can be even faster and better. Hey, we'll continue our interview in a moment after a word from our sponsors.

19:00years. On top of that, we've now layered summary articles describing my relationship with hundreds of contacts, organizations, and ideas. And now that this exists, there's almost nothing that Claude can't help with. For my angel investing, Claude can now draft investment memos in exactly the form that my venture fund requires, based on the calls I've had and the emails I've exchanged with the founders. And when someone needs a favor, Claude can often do it as well as I can. Recently, a friend reached out to ask if I know anyone who might be a fit for a role that he is currently hiring for. Initially, nobody came to mind.

19:37But then I thought to ask Claude. And sure enough, it identified two great leads. Claude is the AI for minds that don't stop at good enough. It's the collaborator that actually understands your entire workflow and thinks with you. So, for problems worth solving, get started with Claude at claude.ai.tcr. That's claude.ai slash TCR. And check out Claude Pro, which includes all of the features mentioned in today's episode. That's claude.ai slash TCR. So now the other sort of complementary AI that's starting to get introduced at the same time is, I think everything we've said so far is more around the validation side.

20:22You could imagine a human sitting there, and maybe you can tell me like some of the shortcuts, again, that already exist before we get to the sort of AI co-pilot or engineer. But you can imagine a person sitting there, I have this vision of my dad doing this from years ago, drawing these little shapes in three-dimensional space and kind of manipulating points in the point cloud. And it was that human intelligence that would say, okay, here's the test result I just got. Here's the spot that the way in which we're having some aerodynamic problems. Let me go back in there, tweak the design a little bit, sand that rough edge down a little bit now of course i've got these other constraints too that i got to keep in mind so i've developed a certain intuition for how i can make these changes without breaking other constraints that are really binding on me as i develop this system now i'll do those changes and how manual those are they were quite manual when i watched my dad do it again you can tell me some of the shortcuts but then they go back into the into this solver right new big thing of course is that the ais are also coming to full iteration cycles i want to hear too about how with the AI co-pilot, like how is that experience changing for engineers?

21:30How automated is it starting to get? How automated is it likely to get in the natural distant future? Yeah, very good points.

21:38Thomas von Tschammer: On the metrics, I can give you a few examples that are public out there. The first one is Jaguar Land Rover, JLARG, automotive OEM out there, based in the UK. they've published their work with us at the latest NVIDIA GTC conference that was back in March this year. And they're using AI for all their external aerodynamic workflows, right? As I was mentioning, a couple of years ago, they were using click-on numerical solvers. And even in your design, they were going through this very expensive solver, with time consuming. And they had already highly parallelized, highly optimized this string. and they got to about 50 designs evaluated every day.

22:22Thomas von Tschammer: So 50 designs and iterations between the studio teams, which are responsible for the aesthetic, the design of the car, and the aerodynamic system. And at the end of the day, it is a trade-off between the best-looking car and the most aerodynamic, because you want to improve range, right? With AI, and that was what the Pamelake announced a couple of months ago, they went from 50 designs evaluated per day to 1 ,500. every single day in production. So you can imagine the level of spin-up that it brought today. We have other suppliers as well that are designing a battery-cooled place to cool the battery that were able to reduce by 80 % their development cycles, becoming 80 % faster to develop a new battery-cooled place.

23:07Thomas von Tschammer: And on top of these smaller design cycles, they could get better performance, right? Because now, if you can explore many more designs, it also means that you can innovate there. you can find new options that you could not think of before as an engineer because you just rely on intuition right and we see examples where the company is cooling 20 percent better and the battery is 15 percent lighter if you think about aerodynamics and country also help engineers to find designs that are two three five percent more aerodynamic which is can change of them as we came Yeah. So let's dig into that last point.

23:47How is that happening? Again, to map this onto a space I've studied in a little bit more depth, there's the protein generation models now, right? Which allow us to go beyond a biologist's ability to say, oh, there's a couple other proteins in the protein bank that are similar to this that I can pull in, or even I have a little intuition of my own. And now we've got models just throwing out new designs, which can then be evaluated. So where are we on this sort of language assistant with like tool calling paradigm to intuition of design? You can imagine a language freeway. You could imagine a version that's just, here's your constraints.

24:31Here's the point cloud. Here's the feedback that we got generated new point cloud. And you could just go point cloud to point cloud. Where are we on that? In those relative paradigms, how are they starting to converge?

24:44Thomas von Tschammer: Yes. Today, what we're seeing is that this AI-driven engineering workforce are not a black box, right? They are today an assistant to the engineer so that the engineer can take the right data and inform design decisions, right? We are not in a world where we're just sending a spec sheet to AI and expect the AI to get back to us as a black box with the best optimized car, right? We are rather in workflows where the AI ingests the spec sheets, understand the requirements. We set up the base model, but this will be done in interaction with the engineer that will validate steps along the way, and will also guide the model.

25:25Thomas von Tschammer: Why is that? It's because engineering is a very complex space where you never have one single answer. It's always trade-offs between disciplines, between costs, between design, between performance, right? So you want to have these domain experts in the loop that then ultimately can take the right train of decisions. But essentially we are moving from a world where, as you were saying with your dad, where in the past you were relying on intuition. Hey, my design doesn't work, but I think if I took it that way manually, it should work out. To a world where now the AI is providing a space of solutions of options to the engineer.

26:02Thomas von Tschammer: And then the engineer will explore that space and decide with which design they want to move forward. And that means that here in that process, what is very interesting is that the AI is able to interact with the different tools. With CAD, we talked about CAD. So now the AI is able to interact with CAD to send your geometries. He's able to interact with numerical simulation solvers. Hey, I want to validate this design I came up with and send that directly to the high-fledged simulation solvers. and all of that becomes automated. So what models are you serving as the co-pilots for engineers today?

26:39We're talking on Fable plus one and we're seeing all these incredible projects that people are just vibe coding their way to where 3D landscapes that are really becoming like extremely elaborate. The models are just kicking out. So it sure seems like there's been some important thresholds crossed in terms of the model's ability to reason physically and use physics coding engines and things like that. I would expect on some level that Mythos class models are like pretty good at just sitting down, so to speak, and using a CAD product. And that sort of performance is probably like pretty hard to find aside from maybe one other provider.

27:28But then I also think, boy, the loop that you want to create is probably one where you can start to train based on the feedback of these validators, which have been the kind of crown jewels of the system so far, which, as you pointed out, embody the company's know-how. What does that look like? How much are we going to be relying on a few frontier models to assist our engineers? how much do you think you're going to start to see this loop get closed and create other specialist models that play that role instead yeah, great question Sox

28:06Thomas von Tschammer: if you think about what I think Jensen mentioned that later he seemed to the AI as a five layer cake from the foundations to the top and the last layer at the very top is the application layer which he explains himself as being the most important one And today in the industry, there are many, many companies that are specializing in building that application layer for some very specific applications. So taking the generic mythos, cloud models, and then bringing the right domain-specific skills, set of abilities, such that these models can have value in very complex environments. This is exactly what we're doing in engineering, essentially.

28:51Thomas von Tschammer: right? So we leverage the standard generic models that are the art that you can think of, right? But we are giving them the right set of tools that they can work with 3D geometries, they can generate new geometries, the right set of skills so that they have the context about what a mill means to generate a geometry, what are injection molding design requirements, if I want to do a mix, what do I need to optimize for the right context, but then they can have an impact. Today, if you just take a plane, you will not be able to go very far because they have no very accurate finished resulting. They, yes, they are becoming more and more realistic for video rendering, for example, but they are nowhere close to solving actual fluid dynamics equations for aerodynamics.

29:41Thomas von Tschammer: Right. So there is a big gap to bridge here. And that's what we're doing with the company. nor I percept. But it sounds like you're expecting reasoning to be mostly provided by frontier models for the foreseeable future. The paradigm you think is winning is increasingly capable general purpose reasoners equipped with the right tools. Exactly. Equipped with the right tools I think is the important parts where these models are able again to interact with CAD and original numerical simulation disorders, but with also other class of models. If we take a step back and we look at our history of the neural concept, how did we start?

30:23Thomas von Tschammer: We started in 2019 by building the first AI model architecture based originally on computer vision that could directly ingest video geometries and learn from physics. This is how we started. It was not LLMs back then, it was a different type of architecture, but these models could learn and directly take as inputs a gap geometry and then predicts aerodynamics, deformation, temperature, and so on and so forth, right? But these are also models that you want as part of your workflow, right? And you want the agents to be able to call these specialized physics-aware models so that you can actually speed up overworld design process.

31:05So it would be helpful to do a little kind of comparison to software. I think audience is super diverse, but one common profile is the sort of AI engineer, software engineer, who's now doing increasingly everything with AI, both in terms of writing the code, but also the products that they're building are increasingly AI-ified. And it seems like we're hitting this point where if you can specify what you want in a clear and accurate way, for a really astonishingly large set of things that people might want, the AIs can just deliver that for you now. And they're also getting obviously pretty good at even flagging the areas where you were ambiguous and they might need your help to make a decision.

31:53So it's putting the premium, of course, on the spec and the clear thinking about what it is that we actually want. I guess I don't really know. Well, intuition in engineering would be that these specs are like better typically than they are in software. I would imagine that there's a more disciplined process culture around saying exactly what we really need, because we know that there, first of all, are like hard realities around things like heat dissipation and strength that we just literally have to have. Whereas in software, we kind of figure we'll patch that on the fly later if it's not scaling the way that it needs.

32:31So if something's like literally breaking, we have the ability to kind of reach in and fix that, even if we're in production, obviously not so with a car. So am I right that there is much better specs? And does that put models in a really just position? Or are there ways in which specs are actually still not so well specified as the naive person might think? and we're relying on a sort of human fuzziness to unpack those such that it remains difficult for AIs in some ways.

33:01Thomas von Tschammer: Good question and answer. Unfortunately, if you are an automotive supplier and OEM today, it's more than latter, right? Specific RSQ, as they call it, about request, quotation, and specification. And it's still not fully streamlined, not fully automated, not fully defined. There are standards that OEMs are trying to impose and sets, right? But there is always human interpretation, especially when, for example, we think about a car, which is such a complex problem with an infinite number of dimensions and constraints. Imagine that if you change the thickness of a single component somewhere under the hood, this might impact the overall engine look.

33:43Thomas von Tschammer: And you have a lot of constraints that are tied together, which means that ultimately it's not as deterministic as one might think, which is why those problems are also extremely complex to solve, right? Yes, starting from a set of specs that the model can read and then translate that into 3D. This is exactly what's happening today, right, already. However, why it is not yet as black boxy as it can be for software engineer, it's because the dimension of the problem is much, much broader, much richer, right? You have many more trade-offs we need to make. And there's never one way to get to the solution, right?

34:20Thomas von Tschammer: which is why it's not gonna replace engineers anytime soon, but it's gonna empower them to be faster actually. It's gonna remove or eliminate the low added value tasks. That's for sure, that's already happening, right? So where the engineer needs to manually set up a new simulation, needs to manually go on the CAD and draw a new design. This is gonna, this is being eliminated as we speak, right? but it's never been a replaced engineer taking those design decisions even the demonstration are you sure what does a bake-off look like there when it comes to sitting down thinking what would be a good product in the market even it's something like high level as that models are getting pretty good and then there's all these steps from this sort of initial ideation to breaking things out into subsystems and thinking about the constraints that each of those has to have, and then throwing these things into actual 3D representations in these systems, setting up all the simulations.

35:29I'm increasingly struggling to find the place where I'm like, in that whole sequence of events, like here's the ones that the AIs can't do. And I think there's something we may not want them to do, where we may want to hold on to final judgment, final calls, all that kind of stuff. But leaving aside what we want to hold on to, it doesn't feel like it's so far off that you could have a little society of fables pick up where the humans left off and literally design the next model year of car. If that's crazy, why do you think that's crazy? What is the part that we're so far off on?

36:12Thomas von Tschammer: I don't know. You have a good point. All of these tasks today, I strongly believe that with the models and capabilities that we have, can be automatically distributed. These models have capabilities to translate any specification into a design and then simulate that design automatically using Inical Solvers. All of that can already today be automated through agents and that's what we're doing as a company. Where I believe there is another level of complexity is the dimension of the problem. right yes this is being done today for specific products right battery cold plates emotor aerodynamics crash right where i don't believe this is this will become a black box is you add all the dimensions you want to have for a card right because then that's where you really want and you're saying we want engineers to hold on to that i believe that you we always want to have engineers all done to the final trade-off and the final decisions, right?

37:13Thomas von Tschammer: Because that's where you need the many expertise deeply ingrained, right? And that's also where the differentiators are happening between you and the competitor, right? How will the differentiation will happen tomorrow between OEM 1 and OEM 2? It's going to be the ones that are able to deeply ingrain their engineering and key into these AI workflows. That's going to be key. That's what many good companies are realizing. Yeah, that's fascinating. What do you think the odds are that we get like, I guess I'll only start with one about your alignment of your business model to the role of the human.

37:51I assume that over time, you've probably had some sort of seat based pricing there and potentially also a sort of compute based pricing for simulation execution. I'd be interested to know where you've been on that historically. But then as we go forward, do you think you actually can sustain a seat-based model or is the model going to have to move in another direction?

38:15Thomas von Tschammer: I think essentially we have to be based value, right? What's the value we're providing, right? And that's the price, essentially, the price for a value ratio, right? The question is, how do you monitor value? And I think it depends on the industry and applications. But ultimately, we're going to move to a world where pricing is going to be based pure value to deliver for companies. And I think that's actually very healthy, right? That's what you want to have. But yeah, because the value is not going to be tied to an individual anymore, to a six, but now potentially to agents, right? Pricing will evolve according.

38:47Thomas von Tschammer: And I think it's going to save every company. How much disruption do you expect to see in these like 100-year-old industries like auto where obviously companies have changed a lot, But it's largely companies that were formed 80, 100 years ago that have consolidated. And there's certainly been only the strong survive dynamic. But not too many new entrants recently, right? Like a couple. But when you think about what matters most being like figuring out a distinctive way to encode your know-how into an AI flywheel process, especially one that like might even enter into its own kind of recursive self-improvement loop.

39:31I just talked to some OpenAI forward deployed engineers last week who are doing this for tax and pace with which they are able to convert feedback from a tax professional on something the AI did wrong into an improved scaffold that prevents that error from happening next time. The case of progress is so fast right now that they're really rapidly climbing the hill, in their case, of accuracy of tax prep documents. But do you think that there's something fundamentally different about manufacturing that would make those hills really hard to climb? Or if you're good at climbing those hills, is it a moment where you actually think we could see new entrants into the market come up and rival the incumbents?

40:17Thomas von Tschammer: for sure i think it's going to be a massive disruption in the markets and i think it's going to create exponential gaps between the companies that are able to adopt these ai-driven engine flows and the ones that are not right and the gap is going to widen essentially over the past the next few years for sure right so there's going to be a lot of disruption what is complicated for companies if you think about traditional oem is that they've been building cars essentially the same way for the past 20 years, right? Same teams, same tools, same know-how, right? So you're asking engineers that have worked the same way in a long time.

40:55Thomas von Tschammer: Now the change they are thinking and even the governance of their own company, right? Seeing if some are embracing it faster than others, right? And our role is to make sure that we can support them in that journey, show them how others are doing it, what are the best practices out there, and move them through the process. But I also believe that yes, there is also an opportunity today for a company that is building hardware to come and to come fast, right? We talk a lot about these digital native companies, right? And we work with many of those outside automotive, right? The electronic products, instrumentary products, right?

41:30Thomas von Tschammer: So those companies have been building hardware for the past 10 years maximum, right? And you feel that they're able to pick up these new workflows much faster. Right. What do you mean by this? I mean that in a year they've had, in the ring presses, massive impact, massive speed up of their developed workforce across many applications. Right. And we think they're a slower pace in the larger, older companies, engineering companies out there. Yeah, that's been the story of Detroit for quite a while. It's been companies that they came up, they got so big and so powerful and had such market dominance that they forgot that they might need to evolve.

42:15And they've come a long way since then. I'm talking that's like a 50 years ago phenomenon. They've come a long way, but they're now going to be faced, I think, with their most profound challenge ever. And competing with Japanese companies in the 70s and 80s is potentially going to be easy mode compared to competing with AI native companies if things go a certain way. So it's going to be really interesting to watch the organizational dynamics and challenges of that.

42:44Thomas von Tschammer: When you think about it, some numbers today, Western Europe or US OEM, it takes them between 40 to 60 months for a new card's room. from the moment they want to launch it to the actual moment it hits the plant and it's being manufactured right in in china it's 18 to 24 months everything those companies in us and europe care about is how do we get from 60 to 24 months right because that's what's going to create the next computer advantage for that yeah that's a sobering stat i mean the number of iterations in a given time is a pretty hard deficit to overcome long-term. Quite sure how to phrase this question, but one thing I wonder about is, in a way, if we really super optimize the design process, it seems like at least in a naive way, we might end up making things really hard on manufacturing.

43:47The sort of, going back to like my dad's, The start of his career, there was literal like pencil on paper and like annotation of it should be this much. Right. And the tolerances that the that existed on the machining side were just a lot more generous than I think they are today. And I think maybe even you could imagine, again, if the design gets so optimized and we're really satisfying these constraints down to the absolute maximum in our designs, it sounds like that would create really super tight tolerances and really super difficult manufacturing challenges. And so how do you think about, I guess maybe one answer is this is the role for the human engineer, but don't satisfy, you know, let's not satisfy ourselves with that answer.

44:31how should we be thinking about like just how far we want to push design optimization and when we need to meet manufacturing a little bit more in the middle it's a great question so when we're

44:44Thomas von Tschammer: saying that we want to optimize designs we also want to optimize them for manufacturing right what do you mean by that that means that when i'm saying that building a car is a multidisciplinary and multi-objective optimization. I also mean that because you want to incorporate design roles, manufacturing constraints as early as possible into your design iterations, right? Because that was the promise of additive manufacturing years ago, right? That you could design freely because then you could print anything, right? But then we quickly realized that this would not work out because it's expensive, it doesn't scale in production.

45:23Thomas von Tschammer: So now, let's say you have a manufacturing plant and you have your stamping process, you know what are the manufacturing constraints. You know what you can and cannot do with stamping. But what you want to make sure of is that those design rules, those manufacturing rules, are embedded and are available for the AI model to consider as quickly as possible. So that whenever the model evaluates, explores in the design, it explores it, knowing that it can be manufactured. And that's a lot of the work that we are doing as well. We are embedding within the model this know-how to make sure that every single design being explored and generated is valid for manufacturing the reasonable costs.

46:06Thomas von Tschammer: So starting with the physics, also manufacturing costs along the way that you want to bring earlier into the deal. If you had to break down the dynamics or realities that give the Chinese companies such an advantage in iteration time. How much of it would you say is on the design side and how much of it is on the manufacturing speed side? Obviously, those things can't be fully decoupled. Hopefully, you kind of get a sense of what I'm getting at. You know, if I show up with, are they doing their design a lot faster in China or are they getting from the point where they have a design that they like to, you're actually rolling a line dramatically faster.

46:45My sense is it's a little bit more the latter probably, but I'm not really sure how to think about what is contributing to that huge advantage.

46:53Thomas von Tschammer: Yeah. Yeah. So I'll review, tend to think it's more than that, right? They're much more agile when it's about rolling out a new plant. Also the way the plant is being operated, right? It is highly, highly automated there. I mean, now you have engineering executives from Europe, from US, traveling to China to understand how they are setting up their plants, right? The key lessons from it. And then going back and applying those best practices in their country. This is happening already today. Right. So the fact for sure. On the design side, I think the benefit that they have is that they can take more risks because, again, they don't have that legacy to work with.

47:29Thomas von Tschammer: Right. Legacy of processes, of tools, and they can just pick what's best out there today and not what was best yesterday. That's a big differentiator. Right. So I do believe they work with best-in-class tools, for sure. They're able to take more risks, again, because they don't have that inertia, that history of developments, and they have less processes that are deeply ingrained. If you don't have processing that were built in the 2000s, it's much easier to work in a much more agile way today. Then we're going back to the digital native type of companies that are able to pick up new standards.

48:06We, the quote-unquote West, have our work cut out for us, it sounds like. how big of a deal is it going to be for neural concept to move from the one model per company based on their data paradigm to a more foundation model type paradigm foundation models obviously could be like with varying breadth right but simply going from one aerodynamic model per company to a general purpose aerodynamic model that would allow you to be like, what if we did a cyber truck and I had to move from your historical product line to something quite different? Seems like it would be a huge value unlock. Then you could also imagine going even more modalities, right?

48:55And trying to bring, I say this all the time, so I apologize to listeners that you haven't heard me say, integration that we see on language and pixels coming out of the nano banana omni sort line does really show to me that integrating quite different data modalities into the same model is a super powerful thing to do. And then there's another version of the foundation model, which is all these constraints generate me the design in the first place, you know, as opposed to, you know, to the validation side, which I was speaking about before. Do you guys have kind of a, how would you describe your strategy on that?

49:32How, how big of a deal is that? How soon do you think we will get there? I assume it's got to be inevitable on some level. What's your sort of strategy to bootstrap into those things?

49:43Thomas von Tschammer: Yeah, yeah. So you're right, it's going to happen, right? It's moving extremely fast already. So we are going to have foundational model for aerodynamics. I think it's going to start with aerodynamics, right? This is the lower English fruit today, because even the physics complex is relatively similar across components, right? It relatively easily replicated. So it is going to happen very quickly. And of course, it's a very big deal for us. So we are doing actually research in that direction. However, we may not be the first ones to have that foundational model. But then, and I'm going back to Jensen's final case of AI, our role and also our focus is to make sure that whatever the foundational model is, we provide the right domain specific capabilities that it can be fully integrated into a complex engineering environment in a hundred thousand people organization right so so that you can visualize your designs you can tweak the geometry as well leveraging those foundational models and so on and so forth so this is also our role and what we are already building towards so that when the foundational capabilities arrive, everyone will be ready for its mission and to leverage day.

50:59One thing I learned in researching neural concepts I thought was super interesting is that you guys are serving, in addition to a bunch of enterprise customers, a number of Formula One teams. This is, I've honestly never really been super into motorsports or engineering sports. don't know a ton about it. But it does strike me that in the run-up to the countries of geniuses in a data center, we really do stand to learn a lot from highly competitive and performance-oriented organizations like Formula One teams that are under just this incredible pressure to turn things around quickly, right? So how does that look like right now?

51:46And another interesting detail was that apparently Formula One teams have a explicit, it's like one of the big rules that they work under is that they can only spend so much compute on aerodynamic simulation. That was a real surprise to learn. Is that just to prevent an arms race? There might be something for like our AI governance listeners to learn from formula one as well but what do you think we should be learning from the what have we seen what should we be learning from the formula one users yeah i mean

52:17Thomas von Tschammer: that that's super interesting right so indeed today formula one teams are kept in to be most specific the cpu hours that they can run so spi always is the compute to run external aerodynamic simulations right and what's even more interesting is that depending on your run key from the previous year you don't get the same numbers always for the next season that you want to try and make it more equal across teams right and you don't want to make it a race as to okay the biggest budget the biggest compute so i just went essentially so they're trying to equalize that in some way so they handicap essentially if you win you get less you get less compute a harder schedule exactly yeah and because the nfl does the nfl does if you're if you're first place you have to play a first place schedule.

53:02The NBA does if you're like, they're now changing it because people have been tanking to try to get better draft picks. But I'd never heard of this level of actually changing not just the talent acquisition process or who the opponents are going to be, but actually changing the fundamental rules of the game itself in terms of how you are allowed to prepare from one race to the next. That is really interesting.

53:26Thomas von Tschammer: Now they can directly tie compute, stimulations that are being run to performance, right? Because if you can run more simulations, as an engineer, you can explore more designs and you can improve your car further, right? Which is, in Formula One, right? You want to get the best car out there for the next chase. So that's a way to balance the performance between different teams, actually. That's super interesting. So what are we seeing in terms of their cultures, their practices that you think will diffuse into broader engineering, ultimately manufacturing culture. If you think about it, Formula One engineers are the state of the art of engineering, the most agile teams you can think of.

54:14Thomas von Tschammer: Design of the car is changing between every single race, right? From one week to another. You don't see it because it's very fine details, but the car is actually different, right? They are improving it week over week. So that means that they have to reach an extreme level of automation, of design iteration processes. We see in Formula One teams, we believe what every single OEM is trying to tend towards, trying to aim for the way they work, the way they iterate, the way they take design decisions. It's a good way for us essentially to prove tests and to stress test our models, our workflows, our platform.

54:53Thomas von Tschammer: right because if it works for an f1 team we can build it for an f1 team we can believe that then can work for the more traditional oems out there essentially so that's that's really the way we're saying it and we're asking those teams really push the limits of the models on the workflows to break phase essentially because that's when the break phase that we see where we have to focus and what we have to develop so maybe again we can make a little bit of analogy to software where we We have the token maxers who are trying to really push the limits on what their agents can do for them and increasingly writing code anymore.

55:32And then, of course, there's a lot of places where we haven't quite caught up. And so we're maybe still writing cold the old fashioned way or like doing a little autocomplete or whatever that's useful and giving a little speed up. But it's still an assist in the old paradigm versus a genuinely new higher level of abstraction as the base place where a human spends their time operates. could you paint a little bit of a picture for analog in engineering what is the f1 person do when they are token maxing what kind of what are these moments of like key decision or sort of judgment on particular trade-offs that actually rise to their level what does that look like and i think we kind of know what the what the old school one looks like so yeah what is what is the token maxing uh f1 engineers life look like today so they will do typically is that

56:24Thomas von Tschammer: they would look at the next race profile right that has more terms than the previous one and then they would associate that to a list of requirements on which you need to improve the car hey we need to make the car better in straight lines for next track stick we have many more straight lines and we know that i have to overtake much more right um so that that's the baseline then they just send that today into engineering requirements in terms of how the car improves and aerodynamics of the car. And then they are running these AI driven workflows, so that are taking those aerodynamic requirements that have information, awareness about the geometry, about the 3D.

57:03Thomas von Tschammer: And then overnight, the AI driven workflow will generate hundreds of thousands of configurations of design options. We'll evaluate them, we'll evaluate the corresponding aerodynamic performances. And on the morning after, the engineer and the aerodynamicist will have a dashboard, a report, interactive. He sees the thousands of points, of data points, on a dashboard, and he can look at the different tradeoffs, look at the corresponding design, and pick the one they want to move forward for the next race, which is next Saturday. So token mapping is essentially thousands, tens of thousands of designs being explored and evaluated overnight.

57:39Thomas von Tschammer: fully automated by these AMOs. Have we seen any surprises come out of that process? Of course, the legendary Move 37. It seems like increasingly with the Mythos class, Fable models, we're starting to see Move 37s might be strong, but they're definitely stepping outside. I had a really interesting experience today or yesterday where I ran a skill that's a very familiar workflow. and Fable stepped outside of the workflow and proactively asked me a bunch of questions. I actually presented a webpage to me to collect information. Not instructed to do that, never been part of the process before. Opus never did anything like that, but it took it upon itself to say, I've got all these inputs, but I think I could use some more inputs to really do a great job.

58:31And here's what I need from you, the human, to really knock this out of the park. That was like a little mini Move 37 in that it was, I've probably done this workflow 50 times over the last few months and nothing like that had ever happened. What's the sort of most Move 37 like thing that we're seeing in engineering? Yeah.

58:52Thomas von Tschammer: We have very similar analogies and I think to be fair, it's one of the favorite part of the jump fuel. That's where it becomes very exciting. We have engineers that are using these workflows, right? So using AI to explore and asking the AI to explore these hundred thousands of configurations overnight. And when they come the morning after, they're looking at the results. And then they're getting back to us and telling us, hey, very impressive. The AI model came up with a design that I would have never thought would be good. If you had shown me this design like this, I would have said, hey, scrap this.

59:27Thomas von Tschammer: This is not going to work. but actually those designs are better than anything we could come up with and now i need to get to get back to the dashboard to understand why it isn't so much better right so i need to rethink my intuition because i didn't think it could be that cut as a design right so you learn as well from these models again because they explore this much richer space they go out of bomb they go beyond their intuition similar to what you were saying that thing so that's where it becomes super interesting because then something really clicks with engineers. They become very excited because they understand that they can also learn from them all.

1:00:01Thomas von Tschammer: How did it get there? How can I learn from it? Because explore new physics or new phenomenon that I was not aware of when I was only working with intuition, essentially. So is it possible that you can try to reverse engineer the design itself from the AI? Yeah, learning from the AI's advances and its occasional leapfrogs. Over us is definitely a really exciting, thrilling, slightly scary part of this new future. Could you give us a little bit more intuition for like how, like just how radical these moments are? It might be a little bit hard for somebody not in the domain to really grok it, but I'd love to try, you know, to get a little bit better sense on kind of, are they really good optimizations?

1:00:53or are they really like stepping out and exploring different regions of the design space that people, you know, because I think what made Move 37 qualitatively so compelling was like, no human would have made that move. Like initially, I think the live stream commentators like thought it was a blunder, right? When we see these surprises in engineering, like how big of a surprise are they? Are we seeing like, oh, that's kind of interesting. I wonder if that could work. or is it like that looks you know kind of crazy um but in defiance of all my intuitions you know it actually does work just try to help me calibrate on how big those surprises are

1:01:33Thomas von Tschammer: yeah so you remain grounded by physics right so you will not reinvent the physics that that's for sure so you will not get completely insane designs breaking the physics because everything is grounded by physics however we've seen scenarios where the engineer is telling us hey there's no way in the world that I would have done that design because I don't think it could work. Right. Now that I know it works, I need to go back to the dashboard and understand why it does. Right. We have the physics as a baseline, we cannot break the physics, it's going to be there. But we've seen occasion scenarios where the engineers thought it was a mistake, thought it was a blunder, right?

1:02:12Thomas von Tschammer: So you had to move 37, but it actually was not. and it let them rethink the way they were approaching the problem and their intuition around the problem. Maybe another way to think about this is how much is it worth? You talked about like value pricing earlier and there's so much discourse right now around are people going to be willing to pay for Mythos class models? Notably the price originally previewed with Mythos has already come down a lot with Fable being significantly less than that original Mythos preview price. I'm not sure how often it is the case that these insights are like readily quantifiable in terms of money.

1:03:00So because it could be a little bit more efficient, that moves the needle on my range. I can say now my car gets this many miles on a charge where it used to be only this many. How much is that worth in the market? obviously a whole other question. So how are people assessing the value of the, especially the, speak to all aspects of it, including the smaller optimizations, but really interested in these sort of move the small scale or move 37 light type moments. How much value do people perceive in it? Are they able to measure it? Are they willing to pay? You know, is, are we going to see people continue to run the neural concept co-pilot, the engineering co-pilot with anything less than a Fable model?

1:03:49Or is it going to be like, nah, you got to pay for the best because that's where all the insights come from and the bill is going through the roof. But like, we have no choice but to do that to stay competitive. Where do you think we're going to land in the short term on this willingness to pay question?

1:04:02Thomas von Tschammer: It's a very good question, Elien. Conceiving the value is key. And I mean, it's a situation we always have also with the companies we work with. Typically when you think about design breakthrough, so much better performance than what you could get before, we have discussion with suppliers that are selling to the big OEMs, the big car OEMs down there. There are several aspects. If you can get a better design, it probably means that you are much more competitive on the market, probably means that you will win more projects, more programs with OEMs and with sexual more countries. If today, let's assume you're building battery-cool plates to cool batteries, right?

1:04:44Thomas von Tschammer: If you can win one more program per year, that's millions, tens of millions of dollars, right? Just one more program on the B50, you're winning every year, right? So that's very tangible. And the other way to see it is, if I can get much faster to a good desire, it took me six months, now it takes me three months, I can spend the other three months optimizing my manufacturing process to reduce the cost as much as possible and I become even more competitive, my clients, right, and this has indirect dollar value as well. If your part is 10 % cheaper than competition because you spent three months, you could allow yourself to spend three months working on the manufacturing process, tuning the details, then you win these one, two, three more programs that are then hundreds of millions of dollars.

1:05:34Thomas von Tschammer: And then if we go on the OEM side, if we can shrink down your development times from 48 to 24 months, that's for millions of developments you're selling, you're selling for every car, right? A car is typically a billion, right, to develop from scratch. But if you get even 20 % of it, the math is pretty quick. Yeah, a lot of opportunity for savings in there. What do you think American car companies, to take one very salient example, or you can broaden it as well, what do you think they should set as their kind of critical milestones, like must-hit accomplishments with AI over the next, let's even just say, one to two years?

1:06:20We know on that timescale that OpenAI is planning to have a very large chunk taken out of ML research itself in terms of automation. We know that we're already iterating at half the speed of the Chinese companies. We see some hard manufacturing places where the product cycle has accelerated, look no further than NVIDIA for, you know, probably the most dramatic example of this. Forget about like what would get you initially like a, you know, coffee spit take and laughed out of the room if you said it. What do you think is like the actual achievable speed up that you would, you know, heart of hearts tell the CEO of GM, like this is what you really need to be able to do in terms of speed up if you want to be meaningfully, you know, durably competitive in the AI era?

1:07:13Thomas von Tschammer: So I think there are different scales, of course, to this. In year one, you'd be looking at some core departments, core areas, crash safety dynamics. and powertrain let's say and you'd want to make sure that every single iteration is being AI-led. AI-led I mean there is an AI workflow that can orchestrate the different tools. That's the base right and that can already lead you to 20%, 30%, 40 % speed up on those flagship disciplines right. That's for year one. Then year two and the year what you'll be looking at is that orchestration across disciplines right. So you break the silos between crash, between error, between thermal So that the agent can not only orchestrate the aerodynamic optimization, but can orchestrate it while taking into consideration the safety aspects, the manufacturing aspects, as we talked before, right?

1:08:05Thomas von Tschammer: Automatic design constraints, right? And break those silos between teams and disciplines. Once you do that, then that's where the gains really compound, the benefits really compound. And that's where you can really break down and reduce the performance cycles from 50 to 60%. And that's what we are seeing already, right? Today, there is not an OEM that was done it at scale for the entire car, right? But we are seeing this multidisciplinary automated AI-driven workflows happening already for some specific disciplines. And we are seeing 50 % speed. That's massive. Yeah, interesting. How do people respond to it?

1:08:46You know, my sense is that you probably won't have a hard time convincing CEOs of these companies that this is really important and they can look again at the China iteration speed. They can look at Tesla's ability to update its manufacturing processes much more dynamically than they're accustomed to doing. And I think increasingly they're going to feel the heat. Now, another question is translating that through a legacy organization where probably a lot of people have a lot of different feelings about this, how they want it to go or if they want it to happen at all. Many of which very understandable feelings, by the way, I don't mean to dismiss those feelings, but they're an obstacle in many cases in the way of the company actually transforming in the way it probably needs to be competitive.

1:09:37How would you characterize maybe kinds of roles? I'm thinking for whatever reason, and the ML researchers are like most keen to automate their own labor. And then we see a lot of artists are very hostile to the technology. Certainly not all, but that's like a pretty common point of view, especially if it's like, I love doing this. You know, why do we want to automate something that I love doing? Where are engineers in that space?

1:10:02Thomas von Tschammer: Yeah, it's a good question. I would, I think, do we sleep both ends of the spectrum, right? I do see that engineers are artists in a way, right? they made their own intuition on how to build a car and they really enjoy that aspect of manual iterations, leveraging intuition, thinking about the physics of the problem. And there are many engineers that's not easy. They've been doing it the same way for the past 30 years and they enjoy that. It's totally understandable that when they see these type of new workflows, they are sometimes a bit resistant as to, okay, what will it actually bring? And they also believe, right?

1:10:40So that they need to be in the loop because ultimately they are the domain experts and

1:10:45Thomas von Tschammer: they are the ones that are the brand of the company, right? Otherwise a GM car would be similar to a Ford would be similar to a BYD, right? So you need to have these similar aspects as well. But we also see all the way, the end of the spectrum, ML researcher, there are also methods team and machine learning teams within those organizations at the forefront of these And those ones are the first ones to adopt it. And those are the first ones that want to explore, want to try new models, frontier models, want to benchmark them, experiment. Right? So we really have in the same organization, those two extremes, right?

1:11:19Thomas von Tschammer: How do you bring them together? That's also part of the complexity of the very large organizations. But the thing we've seen a lot though, is that once you manage to break that I know that barrier and actually have those engineers hands-on and working with the AI models, you see a totally different response because then they understand how powerful it can be and how much it can actually empower them to make their job even better, even funnier, right? Because then they don't have to spend time which is low added value, again setting up simulation, waiting for the simulation for it to load or for it to compute, but they can actually interactively query the AI model, get results, try different options, try much more what you set iOS.

1:12:02Thomas von Tschammer: And that's the fun part when you're an engineer. You want to try these things out. You want to test scenarios, test engines, right? And that's what AI enables you to do today. Sometimes you've mentioned this idea of brands becoming the same and that they need to avoid that happening. This may be sacrilegious to say for a long native Detroiter, but I feel like there's an awful lot of sameness out there in today's world, right? Manufactured products in general, certainly cars, right? And I'm always kind of like, you know, really they look a lot alike. Let's be honest with ourselves. You know, there's been, there's been tremendous convergence.

1:12:46Do you think we're going to see a, or is this maybe even a way to think about success criteria for AI at a societal level, I feel like almost a new trend toward more meaningful differentiation. Yes.

1:13:02Thomas von Tschammer: So I think what happened and I think this trend is going to keep happening in the automotive industry. It's also largely due to autonomous driving, right? It's a bit pushed towards autonomous car because essentially once you've sold autonomous driving, car becomes a commodity, right? So you don't need to own a car anymore, right? If you can just drive to your home, drop you anywhere you want and you don't need drivers, right? Car becoming a community means that ultimately cars would tend to look more the same, right? By definition, by pure definition. However, before we get there, I do believe in the last dependent service I was saying before that the winning companies are the ones that are going to be able to code the best practice brand within these AI workflows.

1:13:48Thomas von Tschammer: And I'm convinced of that. and that can lead to, again, widening the gap between the companies that did not unopt AI quickly enough and the one that did, right? And here we will see massively different differences. People might be surprised by how, when you talk about taking the driver out of the car, so to speak, in a very literal way, that opens up all kinds of new form factors, right? It could be the small delivery car that doesn't have any people at all. It could be sleepers that we get to overnight in on our way to grandmother's house, whatever. I think that has felt, even though people have talked about that, imagine that for a while when they think about the self-driving car world.

1:14:31It has felt like that is a long way off, even once the technology works, because we just haven't seen much change. And why would we expect to see all these form factors pop up from a bunch of car companies that have given us like strikingly point for point similar product lines in recent decades. But this maybe could be very different in a world where a tremendous amount of stuff becomes automated. Is there like a is there a bootstrap there that you think is interesting? One that does strike me is like the vehicle list passengers. in a you know it wouldn't take much to perhaps create some slightly different regulations for those type of devices and next thing you know you could really get a crazy flywheel going perhaps that that then again like leaks out into the rest of the broader industry how like how fast do you think that that kind of stuff could happen i think the main reason

1:15:34Thomas von Tschammer: for the phenomenon you're mentioning is that today, most of the autonomous driving cars are cars that you could drive, but they're not made autonomous, right? And because you need to drive them, then they look like the ones we used to. If you look at Waymo, right? Since it's happening, they take the G-Ward, I pace and they make it autonomous. If you look at another saddle cam, you need to be able to drive it by regulation if you have an issue. But there are a few companies, if I take the counter example, that have built autonomous first vehicles. If you think about Dukes, I guess you know about this company on the West Coast.

1:16:13Thomas von Tschammer: I think the first tagline is that it's not a car, it's a robot actually designed around you, if you recall correctly. Because they built it fully autonomous minds and first, they said, and it doesn't look like a car. even if you think about it it's a different topic anything we've seen so I think the more that technology becomes we measure the bigger the shit can it be to new type of concepts as well how far does this go is there any limit to it you know I kind of imagine a everything is an RL loop as kind of the end state you know you can imagine putting agents into every seat in the company and again we've you know So keep in mind, we're going to need some oversight for this.

1:17:02But in terms of, you know, thinking kind of a first principles limit paradigm first, you know, you could have product strategies, you know, virtual market testing. There's increasingly models that in the same general spirit as you have models that will validate the aerodynamics of your design, you can have models or scaffolds around foundation models that can sort of act as like virtual customer panels. So you could imagine, you know, really from even the highest level, getting into a fast loop that's fed by RL, where you have at least a sufficiently good reward model to steer it in the right direction.

1:17:47And then that can kind of cascade all the way back, perhaps, right, to the designs itself where you can imagine a not-too-distant future where I can sort of speak a perhaps rather complicated mechanical, electromechanical product into existence in a way that I can now like speak a video into existence. Do you see like any fundamental gaps in that vision? Like what, if anything, would prevent that from happening in five years' time?

1:18:27Thomas von Tschammer: No, no, I think the capabilities are there. The main question for big companies to implement that is infrastructure, governance, mostly, right, and data. Obviously, the data flows and it's at the right locations. In terms of capabilities today, we have the right pieces of the puzzle. I strongly believe so. And it's going to go even faster. I will tell you the frontier models that are being developed, right? I don't see any reasons for it not to happen. Again, we won't break the physics. The physics will be there. That's why we need to go to the foundation of tools that are grounded in physics.

1:19:03Thomas von Tschammer: We talked about primal simulation solvers, right? They will be there and they remain there. there would be the surge essentially for these models in engineering right but i don't see any fundamental reasons but i'm quick and i'm actually do you think humanoid robotics or robotics more generally um is critical to this especially on the unlocking the speed on the manufacturing side i mean i think there's a lot of different ways you could imagine unlocking more agility on the manufacturing side um how much do you think things like humanoids matter versus just kind of general intelligence that will you know also design the machine tools and you know kind of bring all the same you can kind of imagine the version that is like built on this if humanoid robots are sort of the analogs of llms you can imagine um that's one kind of way that we get like crazy responsiveness from manufacturing but maybe another is just that again the reasoning models bring the all these same engineering paradigms to the machine tools themselves and that you know is enough to kind of speed things up no so i i do believe that that robots will be in our critical when we think about manufacturing the manufacturing aspects and the plants right now do i believe those need to be humanoid.

1:20:27Thomas von Tschammer: I think it's a different story, right? I think we as humans, we are optimized to do a lot of things. Are we optimized to be in the plants? I'm not sure we're the right form factor. But I think that's a different topic in itself. However, I think there are still some breakthroughs to be done for robots and humanoid to be actively useful at scale within Clans. The first one are the AM models themselves, right? I do believe that we need to make much more advancement into the AM models so that those robots can actively interact with the physical world and be efficient, right? We are far from being there right now in the industry.

1:21:12Thomas von Tschammer: And then there is another question on the hardware side, right? About the autonomy of these robots, how do you make sure they don't overheat right building a hand i don't know if you tell it but there's a lot of research in universities on how do you actually make build a hand that is as agile as what we have uh that is also to some break that is durable you need a lot of breakthrough also on the hardware side we want them to be efficient basket it's gonna happen but i think that there is a there's some work to be done in that area yeah but it sounds like you don't to try to put a little bit finer point on it do you think that we will be fundamentally bottlenecked if we don't have a highly generalizable physical intelligence you know that can kind of walk into a room and like troubleshoot some random you know thing and apply a wrench to a something that you know for a human mechanic that wouldn't be so difficult obviously robots really can't do that very well yet.

1:22:14Do we have to solve that part? Or can we, through sufficient intelligence, kind of take a more top-down route to highly efficient automation that doesn't require this, like, general-purpose physical intelligence to be able to kind of patch things?

1:22:28Thomas von Tschammer: Yeah, yeah. I think I agree. This is just the second order and value, I would say. I think with the first principle, we can solve all these bottlenecks through at least more general automation and intelligence up front, right? The next frontier will be those robots in the planet, right? But I think, to your point, this is definitely so. The future of physical abundance, I think I'm feeling more than perhaps I ever have. I really appreciate you taking the time and walking me through all this and the contribution that you guys are making at Neural Concept is definitely a fascinating one. Anything else that we haven't talked about that you think, you know, I should have asked about, what blind spots would you detect in me that you could help me patch up before I let you get back to work today?

1:23:18Thomas von Tschammer: No, I think that's, we covered the good topics, Nathan. Thanks a lot. I think one thing I can add is that from the outside, I don't think we realize how close we are and how it's actually already happening in the engineering industry today, right? You have engineers, you have products that are being designed AI first and it's true, it's already there, right? And the acceleration is just starting, right? So look out closely at the industry, automotive, aerospace and defense, consumer electronics, look at those industries very closely for the next few years and most likely all the next breakthroughs in terms of designs, performance products, they will be led or they will have an element of AI-driven workflows iterations in them.

1:24:07In the world. Thomas von Schalmer, co-founder of Neural Concept Thank you for being part of the Cognitive Revolution

1:24:32Hey, hey, hey, hey Midnight in the middle Got a mind of its own A million little babies in the marrow and the bone Been drawing the same mind since the world was a stone Now I'm letting go the wheel, let the wild thing go Faster than the fever, finer than the thread It's running down a shape that I never could have said Don't ask me how it knows, when it knows in the dark I just feed it to the fire and it hands me back a spark Don't trust the hand Don't trust the eye Trust the thousand burning through the night Let it run wild, let it run wild Let it run wild now Drop the map, lose the wheel Let it show me how Let it show me how

1:25:32Physics is the boss, ain't no arguing the law But the laws got a loophole and the loophole's wide and raw A hundred thousand corners that a hand never saw Slipping through the maybe like a saint through a wall Like I keep it in a cradle, like I called it a fall Now it's screaming down the straightaway and breaking out the ball Colder than a genius, stranger than a dream Cleaner than a prayer, I never could've skimed Don't trust the hand Don't trust the hand Don't trust the eye Trust the, trust the thousand Burning through the night Let it run wild, let it run wild Let it run wild now Let it run wild, let it run wild Let it run wild now Let it run wild, let it run wild Let it run wild now Drop the map, lose the wheel Let it show me how

1:26:24We don't break the physics We just learn to fly Closer to a perfect We'll never quite describe But let it go, let it climb Somewhere, somewhere And the static is one in a billion design Pick the freak, pick the flaw, pick the beautiful mistake Reverse it, rewrite it, learn the rules it had to break Don't dream like a dream and that's the gift that they gave A wire, that kind of wonder and the code that kind of brave Tear the old map up, throw it in the lake The future's got a thousand other roads to take

1:27:02Let it run wild.

1:27:28Let it run. Let it run now.

1:27:48If you're finding value in the show, we'd appreciate it if you'd take a moment to share it with friends, post online, write a review on Apple Podcasts or Spotify, or just leave us a comment on YouTube. Of course, we always welcome your feedback, guests and topic suggestions, and sponsorship inquiries, either via our website, CognitiveRevolution.ai, or by DMing me on your favorite social network. The Cognitive Revolution is part of the Turpentine Network, a network of podcasts, which is now part of A16Z, where experts talk technology, business, economics, geopolitics, culture, and more. We're produced by AI Podcasting.

1:28:22If you're looking for podcast production help for everything from the moment you stop recording to the moment your audience starts listening, check them out and see my endorsement at AI podcast dot ING. And thank you to everyone who listens for being part of the cognitive revolution.